SIPF: Scale invariant point feature for 3D point clouds

In this paper, we propose a method for detecting Scale-Invariant Point Feature(SIPF) including 3D keypoints Detector and feature descriptor. To detect SIPF, we first estimate a keyscale for point cloud, and calculate the covariance matrix of each 3D point. Keypoints are the saliency points who have a fast change speed along with all principal directions. Then the descriptors are encoded based on the shape of a border or silhouette of an object to be detected or recognized. Experimental results with the Stanford datasets demonstrate that the proposed method can be effectively used for 3D point clouds expression.

[1]  Martial Hebert,et al.  Multi-scale interest regions from unorganized point clouds , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

[3]  Andrew E. Johnson,et al.  Surface matching for object recognition in complex three-dimensional scenes , 1998, Image Vis. Comput..

[4]  Ghassan Hamarneh,et al.  N-Sift: N-Dimensional Scale Invariant Feature Transform for Matching Medical Images , 2007, ISBI.

[5]  Luc Van Gool,et al.  Hough Transform and 3D SURF for Robust Three Dimensional Classification , 2010, ECCV.

[6]  Yu Zhong,et al.  Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[7]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[9]  Kazufumi Kaneda,et al.  3D Keypoints Detection from a 3D Point Cloud for Real-Time Camera Tracking , 2013 .

[10]  Jan-Michael Frahm,et al.  3D model matching with Viewpoint-Invariant Patches (VIP) , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Kazufumi Kaneda,et al.  Scale ratio ICP for 3D point clouds with different scales , 2013, 2013 IEEE International Conference on Image Processing.

[12]  Mohammed Bennamoun,et al.  On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes , 2009, International Journal of Computer Vision.

[13]  Nico Blodow,et al.  Persistent Point Feature Histograms for 3D Point Clouds , 2008 .

[14]  Tamal K. Dey,et al.  Eurographics Symposium on Point-based Graphics (2005) Normal Estimation for Point Clouds: a Comparison Study for a Voronoi Based Method , 2022 .

[15]  Umberto Castellani,et al.  Sparse points matching by combining 3D mesh saliency with statistical descriptors , 2008, Comput. Graph. Forum.

[16]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[17]  R. Horaud,et al.  Surface feature detection and description with applications to mesh matching , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.